The variational autoencoder [1]}, [2]} is a popular deep generative latent variable model that combines the strengths of amortised variational inference and graphical models in order to capture the low-dimensional latent structure of a high-dimensional, complex data distribution. However, VAEs assume that the latent representations are independent, and hence fails to capture the correlations between the data samples [1]}.
